Field of Invention
The present invention relates to a technical field of traffic monitoring, and more particularly to a method for detecting traffic violation.
Description of Related Arts
In large and medium cities, with the development of urbanization, traffic congestion, traffic violation and traffic accidents are also increasing. In order to more effectively control and manage traffic, intelligent traffic has caused wide attention. Traffic information service is an important part of intelligent traffic system functions, which firstly needs to monitor the traffic flow, so as to rapidly and accurately obtain different traffic parameters. With the sudden increase of video sensors, the traditional manual passive monitoring has been far from meeting the needs of monitoring tasks. Therefore, an object of video sensor research is to realize an intelligent automatic monitoring function which is able to take the place of human eyes; particularly a traffic violation detection system based on monitoring videos, which is able to monitor vehicle violation actions and recognize vehicle license plate.
Video-based traffic violation detection system is able to reduce the traffic accidents by regulating the driving behavior of the drivers, in such a manner that the traffic problems caused by traffic regulations are alleviated. Conventionally, video-based traffic violation detection technology comprises: vehicle localizing, vehicle tracking and license plate recognition. In conventional intelligent traffic system, vehicle localizing technology is widely used in traffic monitoring, which mainly comprises moving object localizing and static object localizing. According to a method for localizing moving objects, vehicles on roads are regarded as the moving objects, wherein illumination changes are handled, which is suitable for backgrounds with multi modes and slow changes. However, a moving object is not necessarily a vehicle. Therefore, many researchers use the visual information of the vehicle surface to localizing, which is based on features such as colors, edges and corners to learn a vehicle or a vehicle part model, and then uses a classifier and a generative model to localizing the vehicle. A method for tracking vehicles is based on technologies such as mean shift, Kalman filter and particle filter. In the simplest case, Euclidean distance, size and angle constraints are used to match the object between adjacent frames. Further, the Kalman filter and the particle filter can be used to estimate the object location of the next frame, so as to better complete the tracking process. Object tracking based on mean shift belongs to an object appearance tracking method, which is effective even in traffic congestion. In summary, the conventional traffic video monitoring system still faces the following challenges during engineering application:
1. All-weather monitoring: wherein in different time period of a day, light conditions change largely, especially between day and night; at night, strobe fill lights are commonly employed, so as to see vehicle information within a fill range; but the fill range is usually dozens of meters, which makes it difficult to complete the vehicle tracking, not to mention judgment of vehicle violation.
2. Occluded vehicle: wherein in images, a vehicle may be occluded by other vehicles, or by non-vehicle objects (such as pedestrians, bicycles, trees, and buildings); for example, monitoring camera is arranged at the traffic intersection, vehicles waiting for the signal lamp are easy to block each other; or, a small vehicle is easy to be blocked by a large one, resulting in a short-time detection loss.
3. Pose change: wherein during pose changes of the vehicle, such as turning and driveway changing, road, surface characteristics of in the image is greatly changed.
4. Great intra-class differences and diverse backgrounds: wherein vehicles have different shapes, sizes, and colors; in a complex scene, background objects such as non-motorized vehicles, pedestrians and road traffic facilities are mixed with the vehicle object.
5. Different resolution: wherein during the vehicle passing through the camera view, a pixel number thereof changes largely.
After searching the prior art, it is found that in the Chinese patent 201310251206.7, a method for modeling based on background modeling are disclosed, which extracts a moving vehicle target, and judges violation according a position in a moving foreground. However, during vehicle detection, a moving object is not necessarily a vehicle, and method for modeling based on backgrounds is difficult to handle the occluded vehicle problem. In the Chinese patent 200810240499.8, a vehicle violation detection system is disclosed, which is able to detect running red light, speeding, etc. However, the system needs a pressure sensing device and a speed measuring device besides a camera, for supporting a capture process, resulting in high cost. In summary, the conventional traffic violation detection systems usually use moving information for detection, which has a low monitoring accuracy, and is not able to meet the growing demand for traffic monitoring. Many systems need to be combined with auxiliary equipments such as video cameras and coils, which raises costs. At the same time, during installation and maintenance thereof, traffic must be disrupted and road must be destroyed. Therefore, it is quite difficult to maintain, and maintenance cost is high.